PREDICTIVE ROUTING USING MACHINE LEARNING IN SD-WANs

ABSTRACT

In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) obtains telemetry data from one or more edge devices in the SD-WAN. The service trains, using the telemetry data as training data, a machine learning-based model to predict tunnel failures in the SD-WAN. The service receives feedback from the one or more edge devices regarding failure predictions made by the trained machine learning-based model. The service retrains the machine learning-based model, based on the received feedback.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/362,819, filed on Mar. 25, 2019, entitled PREDICTIVE ROUTING USINGMACHINE LEARNING IN SD-WANs, by Jean-Philippe Vasseur et al., the entirecontents of which are incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to predictive routing using machine learning insoftware-defined wide area networks (SD-WANs).

BACKGROUND

Software-defined wide area networks (SD-WANs) represent the applicationof software-defined networking (SDN) principles to WAN connections, suchas connections to cellular networks, the Internet, and MultiprotocolLabel Switching (MPLS) networks. The power of SD-WAN is the ability toprovide consistent service level agreement (SLA) for importantapplication traffic transparently across various underlying tunnels ofvarying transport quality and allow for seamless tunnel selection basedon tunnel performance characteristics that can match application SLAs.

Failure detection in a network has traditionally been reactive, meaningthat the failure must first be detected before rerouting the trafficalong a secondary (backup) path. In general, failure detection leverageseither explicit signaling from the lower network layers or using akeep-alive mechanism that sends probes at some interval T that must beacknowledged by a receiver (e.g., a tunnel tail-end router). Typically,SD-WAN implementations leverage the keep-alive mechanisms ofBidirectional Forwarding Detection (BFD), to detect tunnel failures andto initiate rerouting the traffic onto a backup (secondary) tunnel, ifsuch a tunnel exits. While this approach is somewhat effective atmitigating tunnel failures in an SD-WAN, reactive failure detection isalso predicated on a failure first occurring. This means that trafficwill be affected by the failure, until the traffic is moved to anothertunnel.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example architecture for predicting tunnelfailures in a software-defined wide area network (SD-WAN);

FIGS. 4A-4B illustrate example plots of packet loss telemetry duringtunnel up and tunnel down events;

FIGS. 5A-5B illustrate example plots of device resource usage duringtunnel up and tunnel down events;

FIG. 6 illustrates an example plot of tunnel down events versus controlconnection down and interface down events;

FIG. 7 illustrates an example plot of precision-recall curves of afailure prediction model for different tunnels;

FIGS. 8A-8C illustrate examples of feedback for tunnel failurepredictions; and

FIG. 9 illustrates an example simplified procedure for predicting tunnelfailures in an SD-WAN.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a supervisoryservice for a software-defined wide area network (SD-WAN) obtainstelemetry data from one or more edge devices in the SD-WAN. The servicetrains, using the telemetry data as training data, a machinelearning-based model to predict tunnel failures in the SD-WAN. Theservice receives feedback from the one or more edge devices regardingfailure predictions made by the trained machine learning-based model.The service retrains the machine learning-based model, based on thereceived feedback.

DESCRIPTION

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics. For the sake ofillustration, a given customer site may fall under any of the followingcategories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/5G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection.

2.) Site Type B: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/5G/LTE connection). A site of type B mayitself be of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/5G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/5G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potentially a backup link (e.g.,a 3G/4G/5G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service levelagreement, whereas Internet links may either have no service levelagreement at all or a loose service level agreement (e.g., a “GoldPackage” Internet service connection that guarantees a certain level ofperformance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/5G/LTE backup link).For example, a particular customer site may include a first CE router110 connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc.

According to various embodiments, a software-defined WAN (SD-WAN) may beused in network 100 to connect local network 160, local network 162, anddata center/cloud 150. In general, an SD-WAN uses a software definednetworking (SDN)-based approach to instantiate tunnels on top of thephysical network and control routing decisions, accordingly. Forexample, as noted above, one tunnel may connect router CE-2 at the edgeof local network 160 to router CE-1 at the edge of data center/cloud 150over an MPLS or Internet-based service provider network in backbone 130.Similarly, a second tunnel may also connect these routers over a4G/5G/LTE cellular service provider network. SD-WAN techniques allow theWAN functions to be virtualized, essentially forming a virtualconnection between local network 160 and data center/cloud 150 on top ofthe various underlying connections. Another feature of SD-WAN iscentralized management by a supervisory service that can monitor andadjust the various connections, as needed.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller/supervisory service located in a data center,etc.), any other computing device that supports the operations ofnetwork 100 (e.g., switches, etc.), or any of the other devicesreferenced below. The device 200 may also be any other suitable type ofdevice depending upon the type of network architecture in place, such asIoT nodes, etc. Device 200 comprises one or more network interfaces 210,one or more processors 220, and a memory 240 interconnected by a systembus 250, and is powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise an SD-WANpredictive routing process 248, as described herein, any of which mayalternatively be located within individual network interfaces.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

SD-WAN predictive routing process 248, detailed further below, includescomputer executable instructions that, when executed by processor(s)220, cause device 200 to predict SD-WAN tunnel failures and proactivelyreroute traffic to different tunnels, prior to their current tunnelsfailing. To do so, in some embodiments, SD-WAN predictive routingprocess 248 may utilize machine learning. In general, machine learningis concerned with the design and the development of techniques that takeas input empirical data (such as network statistics and performanceindicators), and recognize complex patterns in these data. One verycommon pattern among machine learning techniques is the use of anunderlying model M, whose parameters are optimized for minimizing thecost function associated to M, given the input data. For instance, inthe context of classification, the model M may be a straight line thatseparates the data into two classes (e.g., labels) such that M=a*x+b*y+cand the cost function would be the number of misclassified points. Thelearning process then operates by adjusting the parameters a,b,c suchthat the number of misclassified points is minimal. After thisoptimization phase (or learning phase), the model M can be used veryeasily to classify new data points. Often, M is a statistical model, andthe cost function is inversely proportional to the likelihood of M,given the input data.

In various embodiments, SD-WAN predictive routing process 248 may employone or more supervised, unsupervised, or semi-supervised machinelearning models. Generally, supervised learning entails the use of atraining set of data, as noted above, that is used to train the model toapply labels to the input data. For example, the training data mayinclude sample network telemetry that has been labeled as indicative ofan SD-WAN tunnel failure or indicative of normal tunnel operation. Onthe other end of the spectrum are unsupervised techniques that do notrequire a training set of labels. Notably, while a supervised learningmodel may look for previously seen patterns that have been labeled assuch, an unsupervised model may instead look to whether there are suddenchanges or patterns in the behavior. Semi-supervised learning modelstake a middle ground approach that uses a greatly reduced set of labeledtraining data.

Example machine learning techniques that SD-WAN predictive routingprocess 248 can employ may include, but are not limited to, nearestneighbor (NN) techniques (e.g., k-NN models, replicator NN models,etc.), statistical techniques (e.g., Bayesian networks, etc.),clustering techniques (e.g., k-means, mean-shift, etc.), neural networks(e.g., reservoir networks, artificial neural networks, etc.), supportvector machines (SVMs), logistic or other regression, Markov models orchains, principal component analysis (PCA) (e.g., for linear models),singular value decomposition (SVD), multi-layer perceptron (MLP)artificial neural networks (ANNs) (e.g., for non-linear models),replicating reservoir networks (e.g., for non-linear models, typicallyfor time series), random forest classification, or the like.

The performance of a machine learning model can be evaluated in a numberof ways based on the number of true positives, false positives, truenegatives, and/or false negatives of the model. For example, the falsepositives of the model may refer to the number of times the modelincorrectly predicted that an SD-WAN tunnel will fail. Conversely, thefalse negatives of the model may refer to the number of times the modelpredicted normal tunnel operations, when the tunnel actually fails. Truenegatives and positives may refer to the number of times the modelcorrectly predicted whether a tunnel will perform normally or will fail,respectively. Related to these measurements are the concepts of recalland precision. Generally, recall refers to the ratio of true positivesto the sum of true positives and false negatives, which quantifies thesensitivity of the model. Similarly, precision refers to the ratio oftrue positives the sum of true and false positives.

As noted above, failure detection in a network has traditionally beenreactive, meaning that the failure must first be detected beforererouting the traffic along a secondary (backup) path. This is true, notonly for IP and MPLS networks, but also for optical networks (withprotection and restoration) such as SONET and SDH networks. Typically,failure detection leverages either explicit signaling from the lowernetwork layers (e.g., optical failures signaled to the upper layers) orusing a keep-alive mechanism that sends probes at some interval T thatmust be acknowledged by a receiver (e.g., a tunnel tail-end router). Forexample, routing protocols such as Open Shortest Path First (OSPF) andIntermediate System to Intermediate System (IS-IS) use keep-alivesignals over routing adjacencies or MPLS traffic engineering (TE)tunnels. Protocols such as Bidirectional Forwarding Detection (BFD) alsomake use of keep-alive mechanisms.

Traditionally, failure detection in an SD-WAN has relied on thekeep-alive mechanisms of BFD over tunnels, such as IPSec tunnels. Whenthe BFD signaling times out, the tunnel is flagged as failed and trafficis rerouted onto another tunnel. While this approach does help tomitigate the effects of the failure, the reactive nature of thisapproach also means that at least some of the traffic will be lost. Whatis needed is a proactive and predictive approach that is able toidentify SD-WAN tunnel failures before they actually occur.

Predictive Routing Using Machine Learning in SD-WANs

The techniques herein introduce a radical shift for routing in an SD-WANwhereby telemetry is gathered from edge devices (e.g., CE routers, etc.)that relates to failures of tunnel in the SD-WAN along withvariables/parameters that could be used to detect such failure. In someaspects, machine learning is leveraged to forecast such failures (e.g.,identifying parameters with predictive power, computing sampling rates,evaluating PRC performance) either specific to a network, a tunnel, orglobally for a set of networks. In further aspects, the failureforecasting models may be queried globally (e.g., in the cloud) orlocally (e.g., on-premises), according to the required telemetryvariables and their sampling frequency, the resources available onrouter and in the network. In turn, failure predictions may be signaledback to edge device, in the case of global forecasting, and/or reportsof actual false positives or negatives, as well as their contextualdata, may be signaled back to the failure forecasting engine, in thecase of local forecasting.

Specifically, according to one or more embodiments herein, a supervisoryservice for a software-defined wide area network (SD-WAN) obtainstelemetry data from one or more edge devices in the SD-WAN. The servicetrains, using the telemetry data as training data, a machinelearning-based model to predict tunnel failures in the SD-WAN. Theservice receives feedback from the one or more edge devices regardingfailure predictions made by the trained machine learning-based model.The service retrains the machine learning-based model, based on thereceived feedback.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theSD-WAN predictive routing process 248, which may include computerexecutable instructions executed by the processor 220 (or independentprocessor of interfaces 210) to perform functions relating to thetechniques described herein.

Operationally, FIG. 3 illustrates an example architecture 300 forpredicting tunnel failures in an SD-WAN, according to variousembodiments. In general, architecture 300 may be implemented by a device(e.g., device 200 described previously) executing specializedinstructions, such as SD-WAN predictive routing process 248, to providea supervisory service to one or more SD-WAN instances. For example, anSD-WAN controller or other monitoring service may implement architecture300 either locally in the network or as a cloud-based service. As shown,SD-WAN predictive routing process 248 may include the followingcomponents: a telemetry collection module 302, a machine learningfailure forecasting (MLFF) module 304, and/or a model retraining module306. These components 302-306 may be implemented in a distributed manneror implemented as their own stand-alone services, either as part of thenetwork under observation or as a remote service. In addition, thefunctionalities of the components of architecture 300 may be combined,omitted, or implemented as part of other processes, as desired.

SD-WAN predictive routing process 248 may be in communication with anynumber of edge devices 308 (e.g., a first through n^(th) device), suchas CE routers 110, described previously. In various embodiments, edgedevices 308 may be part of the same SD-WAN or, in cases in which process248 is implemented as a cloud-based service, part of any number ofdifferent SD-WANs.

In general, there are many circumstances in a network that can lead totunnel failures in various areas of the network between a head-end andtail-end router (e.g., between routers 110, etc.). An objective of MLFF304, as detailed below, is to learn early signs (networking behavioral)that have some predictive power, allowing the model to predict/forecasta tunnel failure. It is expected that some failures are predictable(i.e., there exist early signs of an upcoming failure) while others willnot be non-predictable (e.g., fiber cut, router crash, etc.). Morespecifically, almost all failures exhibit early signs, but those signsmay appear only a few milliseconds (or even nanoseconds), prior to thefailure (e.g. fiber cut), thereby making forecasting an almostimpossible task. Some non-predictable failures may be due to the absenceof signaling back to the edge device 308 involved and may be localizedto the core of the service provider network (e.g., the underlying IP,4G, 5G, etc. network), in which case the failure is non-predicable fromthe perspective of the edge device 308.

A first aspect of architecture 300 relates to telemetry collectionmodule 302 obtaining the telemetry data required for model training byMLFF module 304. As used herein, the term ‘relevant telemetry’ refers toa telemetry measurement variable with predictive power to predict tunnelfailures, which can be determined dynamically by MLFF module 304.Indeed, failures may be predictable, yet not successfully predicted, dueto a lack of relevant telemetry, the inability of the model to predictthe failure, or the telemetry is sampled at too coarse of a timegranularity. In some embodiments, to obtain relevant telemetry from edgedevices 308, process 248 may send a custom request to one or more ofdevices 308 with the objective of obtaining the list of events ofinterest along with the set of candidate telemetry variables withpotential predictive power to predict tunnel failures. In furtherembodiments, edge devices 308 may instead provide the telemetry data toprocess 248 on a push basis (e.g., without process 248 first requestingthe telemetry data).

In various embodiments, telemetry collection module 302 may adjust theset of telemetry variables/parameters obtained from the edge device(s)308 and/or their sampling frequency. If, for example, MLFF module 304determines that a particular telemetry variable has a strong predictivepower (according to the feature importance, Shapley values, etc.), thefrequency at which such a variable may be gathered may be highercompared to a variable with lower predictive power.

MLFF module 304 may also determine the predictive power of a particulartelemetry variable by assessing the conditional probabilities involved,in further embodiments. By way of example, FIGS. 4A-4B illustrateexample plots of packet loss telemetry during tunnel up and tunnel downevents. As shown in FIG. 4A, plot 400 illustrates the probabilitydistribution function (PDF) of a packet loss measurement for both tunnelup events and tunnel down (i.e., failure) events. In FIG. 4B, plot 410also illustrates a PDF of the packet loss measurement for the two eventtypes. From these plots, it can be seen that the loss measurement is agood predictor of the failure events. Also, as would be appreciated, ahigh p-value indicates that the distribution may differ significantly.

FIGS. 5A-5B illustrate further example plots of device resource usageduring tunnel up and tunnel down events. In plot 500 in FIG. 5A, thememory utilization by an edge device is shown for both tunnel up andtunnel down events. Similarly, plot 510 in FIG. 5B illustrates the CPUutilization by the device during these two event types. By assessing therelationships between tunnel down events and the various telemetryvariables available, MLFF module 304 shown in FIG. 3 can infer that theCPU and memory variables have the highest measures of predictive power,since many tunnel down events are more probable during high CPU ormemory utilization when compared to tunnel up events.

Referring again to architecture 300 in FIG. 3, MLFF module 304 mayselect the set of most relevant telemetry variables. In turn, telemetrycollection module 302 may request that edge devices 308 measure and sendthese variables to process 248 periodically, since real-time variationsof such telemetry is needed for forecasting tunnel down events. Forexample, based on the above conclusion, MLFF module 304 may determinethat the CPU and memory utilizations should be sent periodically (e.g.,every 1 second) by edge devices 308.

Other telemetry variables, such as during a rekey failure when the edgerouter is not able to successfully exchange the security keys with thecontroller, may be requested to be sent to process 248, on occurrence ofthe event. Since such events are rare and the states of the variablesremain the same for longer periods of time, telemetry collection module302 may request an event-based push request, rather than periodicmessages. In other words, telemetry collection module 302 may instructone or more of edge devices 308 to report certain telemetry variablesonly after occurrence of certain events. For example, Table 1 belowshows some example telemetry variables and when an edge device 308 mayreport them to process 248:

TABLE 1 Relevant Telemetry Request Type Memory_utilization Requestedfrom head and tail edge CPU Utilization routers. Periodically once every1 second. BFD Probe Latency, Loss and Jitter Queue statistics (%-agedrops for different queues) Interface down event Rekey exchange failureRouter crash logs Requested from both head and tail edge routers Uponevent occurrence.

In a further embodiment, MLFF module 304 may also attempt to optimizethe load imposed on the edge device(s) 308 reporting the telemetryvariables to process 248. For example, MLFF module 304 may determinethat the CPU and memory usages should be measured and reported everyminute to process 248.

In some cases, MLFF module 304 may also be configured to performroot-cause analysis on the failure events, to identify the root cause ofthe failures. For example, FIG. 6 illustrates an example plot 600 oftunnel down events versus control connection down and interface downevents. From such information, MLFF module 304 may perform root-causeanalysis to infer that there is a causal link between the controlconnection down events and the bfd down (tunnel down) events. In turn,MLFF module 304 may infer that the control connection failures arelikely the root cause of the tunnel down failures and request that edgedevice(s) 308 monitor the root-cause events and report their occurrenceto process 248.

Referring again to FIG. 3, a key functionality of MLFF module 304 is totrain any number of machine learning-based models to predict tunnelfailures in the SD-WAN(s). Preferably, the models are time-series modelstrained centrally (e.g., in the cloud) using the telemetry collected bytelemetry collection module 302. In one instantiation of MLFF module304, the models may be trained on a per customer or per-SD-WAN basis.Testing has shown that model performance may be influenced by parametersspecific to a given network instantiation, thus promoting animplementation whereby MLFF module 304 trains a model for a specificnetwork deployment. In further embodiments, MLFF module 304 may eventrain certain models on a per-tunnel basis. Although such an approachmay be of limited scalability, it may be highly valuable for tunnelscarrying a very large amount of potentially very sensitive traffic(e.g., inter-cloud/data center traffic).

As pointed out earlier, with current reactive routing approaches, recall(i.e., the proportion of failures being successfully predicted) issimply equal to 0, since rerouting is always reactive. In other words,the system reacts a posteriori. As a result, any recall >0 is asignificant gain. One performance metric that MLFF module 304 mayconsider is the maximum recall (Max_Recall) achieved by the model givena precision >P_Min. For example, MLFF module 304 may evaluate thevariability of Max_Recall across datasets, should a single model betrained across all datasets, to determine whether an SD-WAN specific oreven a tunnel specific model should be trained.

In various embodiments, MLFF module 304 may dynamically switch betweenper-tunnel, per-customer/SD-WAN, and global (multiple SD-WAN) approachesto model training. For example, MLFF module 304 may start with the leastgranular approach (e.g., a global model across all customers/SD-WANs)and then evaluate the performance of the global model versus that ofper-customer/SD-WAN models. Such model performance comparison could beeasily evaluated by comparing their related precision-recall curves(PRCs)/area under the curve (AUCs), or the relative Max_Recall, giventhat Precision >P_min.

FIG. 7 illustrates an example plot 700 of precision-recall curves of afailure prediction model for different tunnels of the same SD-WANimplementation. The different curves indicate different splits of thedataset for the same customer and that the performance of the model canvary significantly, even within the same network. This insight can driveMLFF module 304 to train more granular failure prediction models, suchas per-tunnel models for the tunnels for which the current modelperforms poorly.

Referring again to FIG. 3, in some cases, MLFF module 304 may employ apolicy to trigger per-customer/SD-WAN specific model training, if theMax_Recall value improvement is greater than a given threshold. Inanother embodiment, a similar policy approach may be used tospecifically require a dedicated model for a given tunnel according toits characteristic (between router A and router B), the type of trafficbeing carried out (e.g., sensitive traffic of type T, etc.), or theperformance of the global or SD-WAN specific model for that tunnel. Insuch a case, the edge devices 308 may be in charge of observing therouted traffic and, on detecting a traffic type matching the policy,request specific model training by MLFF module 304, to start per-tunnelmodel training for that tunnel.

Prototyping of the techniques herein using simple models and inputfeatures based on coarse telemetry, such as 1-minute averages of loss,latency, jitter, traffic, as well as CPU/memory of CE routers, lead torecalls in the range of a few percent with a precision of 80% or more.More advanced time-series models, such as long short-term memories(LSTMs), especially with attention mechanisms, will achieve even betterperformance. More importantly, using richer and more fine-grainedtelemetry is an important driver of the forecasting performance.

Once MLFF module 304 has trained a prediction model, different optionsexist for its inference location (e.g., where the model is executed topredict tunnel failures). In a first embodiment, model inference isperformed centrally (in the cloud), thus co-located with the modeltraining. In such a case, once MLFF module 304 identifies the set oftelemetry variables with predictive power (used for prediction),telemetry collection module 302 may send a custom message to thecorresponding edge device(s) 308 listing the set of variables along withtheir sampling/reporting frequencies. Note that sampling is a dynamicparameter used by MLFF module 304 computed so as to optimize the PRC ofthe model against the additional overhead of the edge device 308 pushingadditional data to the cloud (and also generating additional logging ofdata on the router).

In another embodiment, MLFF module 304 may push the inference task, andthe corresponding prediction model, to a specific edge device 308, sothat the prediction is preformed on-premise. Such an approach may betriggered by the frequency of sampling required to achieve the requiredmodel performance. For example, some failure types are known to providesignal a few seconds, or even milliseconds, before the failure. In suchcases, performing the inference in the cloud is not a viable option,making on-premise execution of the model the better approach.Inference/model execution is usually not an expensive task on premise,especially when compared to model training. That being said, it mayrequire fast processing on local event with an impact on the local CPU.In yet another embodiment, some models may be executed on premise, ifthe local resources on the router/edge device 308 are sufficient to feedthe local model.

Thus, in some cases, the techniques herein support centralized modeltraining (e.g., in the cloud), combined with the ability to performlocal (on-premise) inference based on the required sampling frequency,local resources available on the edge device 308, as well as thebandwidth required to send the telemetry for input to a model in thecloud. For example, one failure prediction model may require a slowsampling rate but a large amount of data, due to a high number of inputfeatures with predictive power. Thus, reporting these telemetryvariables to the cloud for prediction may consume too much WAN bandwidthon the network. In such a case, MLFF module 304 may take this constraintinto account by evaluating the volume of required telemetry, accordingto the sampling frequency, and the WAN bandwidth allocated on thenetwork for the telemetry traffic. To that end, MLFF module 304 mayanalyze the topology of the network and the available bandwidth fortelemetry reporting (e.g., according to the QoS policy). If thebandwidth available for the telemetry used for the inference of themodel exceeds the capacity, MLFF module 304 may decide to activate alocal inference by pushing a prediction model to one or more of edgedevices 308.

In yet another embodiment, MLFF module 304 may take a mixed approachwhereby some of edge devices 308 perform the inferences locally, whileothers rely on SD-WAN predictive routing process 248 to perform thepredictions.

A further embodiment of the techniques herein introduces a feedbackmechanism whereby feedback regarding the predictions by a trained modelis provided to model retraining module 306. In cases in which the modelis executed on an edge device 308, the edge device 308 may report therate of false positives and/or false negatives to model retrainingmodule 308. Optionally, the reporting can also include additionalcontext information about each false positive and/or false negative,such as the values of the telemetry variables that led to the incorrectprediction. If the performance of the model is below a designatedthreshold, model retraining module 306 may trigger MLFF module 304 toretrain the model, potentially increasing the granularity of the model,as well (e.g., by training a tunnel-specific model, etc.). In cases inwhich MLFF module 304 trains multiple prediction models, modelretraining module 306 may evaluate the performance of each model and,based on their performances, decide that a particular one of the modelsshould be used. Such an approach allows MLFF module 304 to dynamicallyswitch between models, based on the data pattern currently beingobserved.

When failures are predicted in the cloud by SD-WAN predictive routingprocess 248, model retraining module 306 may similarly receive feedbackfrom edge devices 308 regarding the predictions. For example, once amodel M predicts the failure of a tunnel at a given time, MLFF module304 may send a notification to the affected edge device 308 indicatingthe (list of) tunnel(s) for which a failure is predicted, along with thepredicted time for the failure, and other parameters such as the failureprobability Pf (which can be a simple flag, a categorical variable (low,medium, high) or a real number). The edge device 308 may use Pf todetermine the appropriate action, such as pro-actively rerouting thetraffic that would be affected by the failure onto a backup tunnel. Inone embodiment, the predicted failure may be signaled to the edge device308 using a unicast message for one or more tunnels, or a multicastmessages signaling a list of predicted failure to a set of edge devices308.

Regardless of how model retraining module 306 receives its feedback,either from the edge device 308 executing the prediction model or fromMLFF module 304 executing the model, model retraining module 306 maydynamically trigger MLFF module 304 to retrain a given model. In oneembodiment, the model re-training may be systematic. In anotherembodiment, upon reaching a plateau in terms of improvement forMax_Recall or Max_Precision, model retraining module 306 may reduce thefrequency of the model training.

FIGS. 8A-8C illustrate examples of feedback for tunnel failurepredictions, in various embodiments. As shown in example implementation800 in FIGS. 8A-8B, assume that the trained model is executed in thecloud by SD-WAN predictive routing process 248. In such a case, process248 may send a sampling request 802 to an edge device 308 that indicatesthe telemetry variables to sample and report, as well as the determinedsampling/reporting period(s) for those variables. In turn, edge device308 may report the requested telemetry 804 to process 248 for analysis.For example, process 248 may request that edge device 308 report is CPUload every minute to process 248, to predict whether the tunnelassociated with edge device 308 is predicted to fail. More specifically,process 248 may use telemetry 804 as input to its trained predictionmodel, to determine whether telemetry 804 is indicative of a tunnelfailure that will occur in the future.

When SD-WAN predictive routing process 248 determines that a tunnelfailure is predicted, it may send a predicted failure notification 806to edge device 308 that identifies the tunnel predicted to fail, thetime at which the failure is expected to occur, and potentially theprobability of failure, as well. Depending on the timing and probabilityof failure, edge device 308 may opt to reroute the affected traffic, ora portion thereof, to a different tunnel. In turn, edge device 308 maymonitor the tunnel predicted to fail and provide feedback 808 to process248 indicating whether the tunnel actually failed and, if so, when.Process 248 can then use feedback 808 to determine whether modelretraining should be initiated, such as by training a more granularmodel for the SD-WAN instance or the specific tunnel under scrutiny.

FIG. 8C illustrates an alternate implementation 810 in which SD-WANpredictive routing process 248 pushes the failure prediction model toedge device 308 for local/on-premise inference. For example, process 248may opt for edge device 308 to perform the local inferences, such aswhen model 812 requires too much bandwidth to send the needed telemetryto process 248 for cloud-based prediction. In turn, edge device 308 mayuse the corresponding telemetry measurements as input to trained model812 and, if a failure is predicted, perform a corrective measure such asproactively rerouting the traffic to one or more other tunnels. Inaddition, edge device 308 may provide feedback 814 to process 248 thatindicates false positives and/or false negatives by the model. Forexample, if edge device 308 reroutes traffic away from a tunnelpredicted by model 812 to fail, and the tunnel does not actually fail,edge device 308 may inform process 248. Process 248 may use feedback 814to determine whether model 812 requires retraining, such as by adjustingwhich telemetry variables are used as input to the model, adjusting thegranularity of the training (e.g., by using only training telemetry datafrom the tunnel, etc.), or the like.

FIG. 9 illustrates an example simplified procedure for predicting tunnelfailures in an SD-WAN, in accordance with one or more embodimentsdescribed herein. For example, a non-generic, specifically configureddevice (e.g., device 200) may perform procedure 900 by executing storedinstructions (e.g., process 248), to provide a supervisory service toone or more SD-WANs. The procedure 900 may start at step 905, andcontinues to step 910, where, as described in greater detail above, thesupervisory service may obtain telemetry data from one or more edgedevices in the SD-WAN. For example, the supervisory service mayassociate tunnel failures in the SD-WAN(s) with different telemetryvariables that can be measured, to assign measures of predictive powerto the telemetry variables. In turn, the service may select one or moreof the telemetry variables and a sampling frequency, based in part ontheir associated measures of predictive power, and instruct the one ormore edge devices to report the selected one or more telemetry variablesto the supervisory service at the selected sampling frequency. In somecases, the sampling frequency can also be selected based in part on theexpected load on the edge device(s) that would result from the sampling.In further cases, the supervisory service may request that the edgedevice(s) report the occurrences of certain events that may beindicative of an impending tunnel failure.

At step 915, as detailed above, the supervisory service may train, usingthe telemetry data as training data, a machine learning-based model topredict tunnel failures in the SD-WAN. For example, the model may be atime series-based model that predicts tunnel failures, based on thevalues of the telemetry data. In various embodiments, the supervisoryservice may train the model using training data for a particular tunnel,a particular SD-WAN, or may even perform global training of the modelusing telemetry data from a plurality of SD-WANs overseen by thesupervisory service. Such training can also be performed in a dynamicmanner, so as to increase or decrease the granularity of the model, asneeded.

At step 920, the supervisory service may receive feedback from the oneor more edge devices regarding failure predictions made by the trainedmachine learning-based model, as described in greater detail above. Suchfeedback may indicate, for example, false positive and/or falsenegatives by the model. In some embodiments, the service may opt toexecute the model on the edge device(s) themselves. For example, such aselection may be based on the overhead in reporting the needed telemetrydata to the service for prediction, the amount of time needed for theprediction (e.g., some telemetry may indicate a tunnel failure willoccur on the order of seconds or less), or the like. In furtherembodiments, the service may execute the model using telemetry datareported to the service by the edge device(s). If the service predicts atunnel failure, it may indicate the predicted tunnel failure to the oneor more edge devices, to allow the edge device(s) to perform correctivemeasures, such as rerouting the traffic to another tunnel.

At step 925, as detailed above, the service may retrain the machinelearning-based model, based on the received feedback. For example, ifthe precision or recall of the model is below a threshold, it mayretrain the model until the threshold precision or recall is achieved.Model retraining may also entail adjusting the granularity of the model,such as by training a model that is specific to the SD-WAN underscrutiny or even for a particular tunnel in the SD-WAN. Procedure 900then ends at step 930.

It should be noted that while certain steps within procedure 900 may beoptional as described above, the steps shown in FIG. 9 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, introduce an architecturefor predicting SD-WAN tunnel failures and performing reactive routingbefore the failures occur.

While there have been shown and described illustrative embodiments thatprovide for predicting SD-WAN tunnel failures, it is to be understoodthat various other adaptations and modifications may be made within thespirit and scope of the embodiments herein. For example, while certainembodiments are described herein with respect to using certain modelsfor purposes of predicting tunnel failures, the models are not limitedas such and may be used for other functions, in other embodiments. Inaddition, while certain protocols are shown, other suitable protocolsmay be used, accordingly.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: instructing, by a deviceexecuting a software-defined wide area network (SD-WAN) predictiverouting process for an SD-WAN, one or more edge devices in the SD-WAN toreport telemetry data to the device at a selected sampling frequency;obtaining, by the device, the telemetry data from the one or more edgedevices according to the selected sampling frequency; training, by thedevice and using the telemetry data as training data, a machinelearning-based model to predict tunnel failures in the SD-WAN; aftertraining the machine learning-based model, receiving, at the device,feedback from the one or more edge devices regarding failure predictionsmade by the machine learning-based model; and retraining, by the device,the machine learning-based model, based on the feedback received fromthe one or more edge devices.
 2. The method as in claim 1, whereinobtaining the telemetry data from the one or more edge devices in theSD-WAN comprises: associating tunnel failures with telemetry variables,to assign measures of predictive power to the telemetry variables; andselecting one or more of the telemetry variables and the selectedsampling frequency, based in part on their associated measures ofpredictive power.
 3. The method as in claim 2, wherein the selectedsampling frequency is selected based in part on a computational loadimposed on the one or more edge devices by reporting the telemetry data.4. The method as in claim 1, further comprising: after training themachine learning-based model, deploying, by the device, the machinelearning-based model to a particular one of the one or more edgedevices, wherein the feedback is indicative of false positives or falsenegatives by the machine learning-based model.
 5. The method as in claim1, further comprising: predicting, by the device and using the machinelearning-based model, a tunnel failure of a particular tunnel in theSD-WAN; and indicating, by the device, the tunnel failure that ispredicted to the one or more edge devices, wherein the feedback isindicative of whether the tunnel failure occurred.
 6. The method as inclaim 5, wherein the one or more edge devices reroute traffic from theparticular tunnel to another tunnel in the SD-WAN, based on the tunnelfailure that is predicted.
 7. The method as in claim 1, wherein thedevice retrains the machine learning-based model until a thresholdprecision or recall is achieved.
 8. The method as in claim 1, whereinthe telemetry data is for a plurality of tunnels in the SD-WAN, themethod further comprising: determining that a tunnel-specific modelshould be trained for a particular tunnel of the plurality of tunnels;and training the machine learning-based model to predict failures of theparticular tunnel, using the telemetry data for the particular tunnel.9. The method as in claim 1, wherein the machine learning-based model isfurther trained using telemetry data from edge devices in a plurality ofother SD-WANs.
 10. An apparatus, comprising: one or more networkinterfaces to communicate with one or more software-defined wide areanetworks (SD-WANs); a processor coupled to the one or more networkinterfaces and configured to execute one or more processes; and a memoryconfigured to store an SD-WAN predictive routing process that isexecutable by the processor, the SD-WAN predictive routing process whenexecuted configured to: instruct one or more edge devices in an SD-WANto report telemetry data to a device at a selected sampling frequency;obtain the telemetry data from the one or more edge devices according tothe selected sampling frequency; train, using the telemetry data astraining data, a machine learning-based model to predict tunnel failuresin the SD-WAN; after training the machine learning-based model, receivefeedback from the one or more edge devices regarding failure predictionsmade by the machine learning-based model; and retrain the machinelearning-based model, based on the feedback received from the one ormore edge devices, wherein the apparatus comprises the device.
 11. Theapparatus as in claim 10, wherein the apparatus obtains the telemetrydata from the one or more edge devices in the SD-WAN by: associatingtunnel failures with telemetry variables, to assign measures ofpredictive power to the telemetry variables; and selecting one or moreof the telemetry variables and the selected sampling frequency, based inpart on their associated measures of predictive power.
 12. The apparatusas in claim 11, wherein the selected sampling frequency is selectedbased in part on a computational load imposed on the one or more edgedevices by reporting the telemetry data.
 13. The apparatus as in claim10, wherein the SD-WAN predictive routing process when executed isfurther configured to: after training the machine learning-based model,deploy the machine learning-based model to a particular one of the oneor more edge devices, wherein the feedback is indicative of falsepositives or false negatives by the machine learning-based model. 14.The apparatus as in claim 10, wherein the SD-WAN predictive routingprocess when executed is further configured to: predict, using themachine learning-based model, a tunnel failure of a particular tunnel inthe SD-WAN; and indicate the tunnel failure that is predicted to the oneor more edge devices, wherein the feedback is indicative of whether thetunnel failure occurred.
 15. The apparatus as in claim 14, wherein theone or more edge devices reroute traffic from the particular tunnel toanother tunnel in the SD-WAN, based on the tunnel failure that ispredicted.
 16. The apparatus as in claim 10, wherein the apparatusretrains the machine learning-based model until a threshold precision orrecall is achieved.
 17. The apparatus as in claim 10, wherein thetelemetry data is for a plurality of tunnels in the SD-WAN, and whereinthe SD-WAN predictive routing process when executed is furtherconfigured to: determine that a tunnel-specific model should be trainedfor a particular tunnel of the plurality of tunnels; and train themachine learning-based model to predict failures of the particulartunnel, using the telemetry data for the particular tunnel.
 18. Theapparatus as in claim 10, wherein the machine learning-based model isfurther trained using telemetry data from edge devices in a plurality ofother SD-WANs.
 19. A tangible, non-transitory, computer-readable mediumthat stores program instructions causing a device to execute asoftware-defined wide area network (SD-WAN) predictive routing processcomprising: instructing, by the device, one or more edge devices in anSD-WAN to report telemetry data to the device at a selected samplingfrequency; obtaining, by the device, the telemetry data from the one ormore edge devices according to the selected sampling frequency;training, by the device and using the telemetry data as training data, amachine learning-based model to predict tunnel failures in the SD-WAN;after training the machine learning-based model, receiving, at thedevice, feedback from the one or more edge devices regarding failurepredictions made by the machine learning-based model; and retraining, bythe device, the machine learning-based model, based on the feedbackreceived from the one or more edge devices.
 20. The tangible,non-transitory, computer-readable medium as in claim 19, furthercomprising: predicting, by the device and using the machinelearning-based model, a tunnel failure of a particular tunnel in theSD-WAN; and indicating, by the device, the tunnel failure that ispredicted to the one or more edge devices, wherein the feedback isindicative of whether the tunnel failure occurred.